International experience in determining the cost-effectiveness thresholds
Bibliographic record
Abstract
The article reviews international methodological guidelines, regulatory documents and existing approaches to the determination of the costeffectiveness threshold (CeT), also known as the willingness-to-pay threshold (WTP), the threshold value of the incremental cost-effectiveness ratio (ICeR), in europe (england and Wales, Scotland, Ireland, France, Belgium, Denmark, the netherlands, Germany, Sweden, Finland, norway, Poland), America (the USA, Canada, Brazil), Asia (Japan, South korea, Taiwan, Thailand), in Australia and new Zealand. The CeT is commonly used to rationalize decision-making in health cost reimbursement. The present review demonstrates that just a few countries (englandandWales,Thailand,Poland,USA) have introduced the explicit value of CeT into their decision making. Some countries (Australia,Canada,new Zealand, thenetherlands,Sweden, andBrazil) use CeT in an implicit manner implying that no specific CeT value is defined by law. In other countries (Finland,Sweden,norway,France,Germany,Denmark,Japan,South korea,Taiwan), the role of the threshold in health reimbursement remains uncertain despite the presence of HTA systems. The CeT is expressed as additional cost per unit of incremental health benefit, which is represented by quality-adjusted life year (QALY) in most counties. However,PolandandBrazilallow using life years gained (LYG) as a measure of additional benefit neglecting the quality of life. In thenetherlandsandengland, different CeT values are applied to the health technology under assessment depending on the severity or rareness of the disease and some other factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.189 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".